Graphics rendering operations are computationally demanding tasks, and designers are often exploring different techniques for simplifying these operations in a manner which reduces the amount of computation required by still produces rich, high quality graphics and images. Light transport simulations are one approach used in visual computing and computer graphics applications such as ray tracing to render the intensity or color value of each pixel of an image. Conventional algorithms treat light transport as an integration problem in the space of all possible paths, and try to solve it using Markov Chain Monte Carlo (MCMC) methods which sample directly from the equilibrium light transport distribution. Examples of these conventional algorithms include Metropolis Light Transport (MLT) and Energy Redistribution Path Tracing (ERPT). However, due to the highly multi-modal nature of the light transport equilibrium distribution, these algorithms suffer from poor mixing in many difficult situations in which the Markov Chains tend to get stuck in local maxima. Consequently, the light transport simulation performed per pixel is relatively slow to converge, and as a result, the complete image, which may include millions of pixels, is consequently slow to render.
Accordingly, an improved system and method for estimating pixel intensity is needed.